“Data analytics is the future, and the future is NOW! Every mouse click, keyboard button press, swipe, or tap is used to shape business decisions. Everything is about data these days. Data is information, and information is power.”

– Radi, data analyst at CENTOGENE

Enterprises of all sizes and sectors work to leverage the value of data for enhanced business success in a digital economy. A significant enabler for a company to become data-driven is data architecture. Designing, developing, and optimizing data-driven systems while taking into account the company’s vision, strategy, business rules, standards, and data management skills.

Why is data architecture important to the business? When it comes to managing data across the whole data lifecycle of data capture, integration, analytics, and visualization, data architecture provides firms with sound methodologies. Particularly, data architecture provides the following three advantages to the company:

  • Effective data approach: In order to choose the optimal option, the strategy generally includes weighing your options and making trade-offs. A solid data strategy must be supported by a flexible and scalable data architecture that is in line with business objectives, legal obligations, operational guidelines, technical capabilities, and IT standards that reflect both the present and future states of the industry. It is critical to understand enterprise data management and its component disciplines since these characteristics are mirrored in the data strategy, which also includes all of the enterprise data management components. A view of the data landscape utilizing an enterprise data model should be part of a successful enterprise data architecture (EDM). An EDM is a comprehensive illustration of all the data produced and used by the company as a whole.
  • Collaboration and Communication Improvements: In a normal business, there are a variety of stakeholders with varying responsibilities, demands, goals, and restrictions across several different business lines. Enterprises also function under a variety of presumptions and limitations. Therefore, it is crucial to have a data architecture that can offer a common language for better communication, cooperation, and data literacy when stakeholders from multiple roles, geographies, LoBs, and competing needs come together.
  • Optimal information flows: Data architecture offers chances for developing lean and effective information flows by removing complexity, reusing data, and decreasing data and system redundancy since it offers a comprehensive perspective of the enterprise’s data flows—both current and future. In the end, this leads to lower costs, less risk, and a quicker time to market for the goods and services.

What would the company stand to lose by not having a sound data architecture? Data can be an asset, but it can also rapidly turn into a problem. Data can become a liability to the business in three different circumstances, all of which can be linked to poor data architecture:

  • Undefined Objective: Companies frequently gather data without a defined business aim in the absence of a good data architecture. Without a clear goal in mind, data collection will be more expensive and lead to lost commercial possibilities. 73% of data in an organization, according to Forrester, is never used strategically. Even while it takes a lot of time and effort to collect, store, and safeguard data, the opportunity cost of not using the data in today’s digital environment is enormous.
  • Poor Compliance with Laws, Rules, and Ethics: Data architecture offers ways to deal with legal, business, industry, and even ethical compliance. Companies today must ensure strong data security and privacy due to the increase in cybercrime and data breaches. A data architecture must take data security and privacy into account. When hackers gained access to millions of consumer records from the credit reporting company Equifax in 2017, the company spent $1.4 billion overhauling its security infrastructure since Equifax’s inadequate data and security architecture was the main contributor to the problem.
  • Increased Costs: Companies risk wasting time and resources maintaining duplicated data throughout the data lifecycle, such as duplicate customers, products, assets, etc., without an enterprise-level data architecture. In addition to human costs, data management uses a lot of the data center’s electricity, which increases the company’s carbon footprint. Data centers used roughly 1.1% of the total electricity produced worldwide in 2018.

To ensure data-driven capabilities, every business should design, build, and maintain an enterprise data architecture. Each organization should create and implement an enterprise data strategy that covers all aspects of enterprise data management in addition to the data architecture. By doing this, the company will be able to gather, manage, store, and use data as an asset.

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